Today's hearing aids are usually provided with a signal processor and a number of different signal processing algorithms, wherein each algorithm is tailored to particular user preferences and particular categories of sound environment. Signal processing parameters of the various signal processing algorithms are typically determined during an initial fitting session in a dispenser's office and programmed into the hearing aid by activating desired algorithms and setting algorithm parameters in a non-volatile memory area of the hearing aid and/or transmitting desired algorithms and algorithm parameter settings to the non-volatile memory area.
Typically, an audiologist spends a very limited amount of time on fitting a hearing aid to each patient compared to all the nuances that are associated with hearing loss. Diagnostic procedures exist which would optimize the prescribed hearing aid parameters to maximize the benefit that the patient would get out of their hearing instruments. Unfortunately, the time needed to carry out these procedures is prohibitive for the audiologist and instead they often resort to an automatic fitting procedure with minimal personalization. This results in several return visits to the audiologist for the patient, alternatively that the patient gives up and deems the hearing instrument as being more of a burden than a benefit and the instrument ends up not being used.
Another fundamental challenge is that the fitting procedure is based on a parametric model defined by the hearing aid manufacturer. This model can be based on e.g. loudness perception, cochlear compression modelling and/or audibility threshold shifts. This implies that the solution space and the possible hearing aid configurations are limited to what the designing scientists think they know about hearing loss, or essentially how good the hearing loss model is in predicting listening performance of the individual patient.
It is known from several studies that the hearing loss model that is typically used is fundamentally wrong. For instance, if the hearing aid is fitted to compensate exactly for the modelled loss of compression in the cochlea, the sound will be uncomfortably loud, which indicates that the model is flawed. Another example of where the model breaks down is when trying to fit hearing impaired subjects with similar or close to identical audiograms but different levels of cognition; here, the higher performing subjects benefit from syllabic compression whereas the lower performing patients benefit more from longer time constants in the compression. The challenge is that the optimization of the hearing aid is based on adjusting a model that is believed to be correlated with listener performance, when it really isn't.
Also, a parametric model does not have the ability to change fundamental behaviour even if new knowledge is unveiled that change the nature of the data.